package com.example.ragproject.config;

import com.example.ragproject.store.MongoChatMemoryStore;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.ChatMemory;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

import java.util.Arrays;
import java.util.List;

@Configuration
public class XiaozhiAgentConfig {


    @Autowired
    private MongoChatMemoryStore mongoChatMemoryStore;

    @Bean(name = "chatMemoryProviderXiaozhi")
    public ChatMemoryProvider chatMemoryProviderXiaozhi(){
        return messageId -> MessageWindowChatMemory.builder().id(messageId).maxMessages(20).chatMemoryStore(mongoChatMemoryStore).build();
    }


    @Bean
    ContentRetriever contentRetrieverXiaozhi(){
        //使用FileSystemDocumentLoader读取指定目录下的知识库文档
        //并使用默认的文档解析器对文档进行解析
        Document document1 = FileSystemDocumentLoader.loadDocument("D:\\knowledge\\医院信息.md");
        Document document2 = FileSystemDocumentLoader.loadDocument("D:\\knowledge\\科室信息.md");
        Document document3 = FileSystemDocumentLoader.loadDocument("D:\\knowledge\\神经内科.md");
        List<Document> documents = Arrays.asList(document1,document2,document3);

        //使用内存向量存储
        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
        EmbeddingStoreIngestor.ingest(documents,embeddingStore);

        //从嵌入存储里检索和查询内容相关的信息
        return EmbeddingStoreContentRetriever.from(embeddingStore);

    }

}
